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Understanding human reposting patterns on Sina Weibo from a global perspective

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  • Yao, Weiyi
  • Jiao, Pengfei
  • Wang, Wenjun
  • Sun, Yueheng

Abstract

Quantitative analysis of human reposting patterns on social networking sites will provide helpful insights into the tracking and management of information spreading and social contagion. A large amount of efforts have been made on mining and explaining the patterns recently. However, existing researches mainly focused on specific events, topics or individuals. A more macroscopic angle is required for understanding the reposting characteristics on SNSs. In this paper, we explore the human reposting patterns from a global perspective by analyzing the large-scale reposting networks, which are constructed by about 1.5 billion reposting records collected from Sina Weibo over 15 months. Experiments indicate that the times of being reposted obey power law decays, and the frequency of reposting between two users follows a distribution of exponentially truncated power law. Meanwhile, taking user categories and micro-blog contents into account, we classify users into three categories and find that different users have distinct habits when reposting micro-blogs, e.g. common individuals would like to repost micro-blogs about entertainments, good qualities and sharing life styles.

Suggested Citation

  • Yao, Weiyi & Jiao, Pengfei & Wang, Wenjun & Sun, Yueheng, 2019. "Understanding human reposting patterns on Sina Weibo from a global perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 374-383.
    • Handle: RePEc:eee:phsmap:v:518:y:2019:i:c:p:374-383
    DOI: 10.1016/j.physa.2018.11.043
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    References listed on IDEAS

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    1. Li, Yuan & Gao, Haoyu & Yang, Mingmin & Guan, Wanqiu & Ma, Haixin & Qian, Weining & Cao, Zhigang & Yang, Xiaoguang, 2015. "What are Chinese talking about in hot weibos?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 546-557.
    2. Yang, Dong & Chow, Tommy W.S. & Zhong, Lu & Tian, Zhaoyang & Zhang, Qingpeng & Chen, Guanrong, 2018. "True and fake information spreading over the Facebook," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 984-994.
    3. Ko, J. & Kwon, H.W. & Kim, H.S. & Lee, K. & Choi, M.Y., 2014. "Model for Twitter dynamics: Public attention and time series of tweeting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 142-149.
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    Cited by:

    1. Li, Pei & Liu, Ke & Li, Keqin & Liu, Jianxun & Zhou, Dong, 2021. "Estimating user influence ranking in independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

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